Summary of Self-comparison For Dataset-level Membership Inference in Large (vision-)language Models, by Jie Ren et al.
Self-Comparison for Dataset-Level Membership Inference in Large (Vision-)Language Models
by Jie Ren, Kangrui Chen, Chen Chen, Vikash Sehwag, Yue Xing, Jiliang Tang, Lingjuan Lyu
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper proposes a novel approach to detect unauthorized use of copyrighted materials in large language models (LLMs) and vision-language models (VLMs). The existing methods for membership inference attacks rely on distinguishing member data from non-member data by leveraging the model’s memorization and confidence patterns. However, these methods are not effective when applied to LLMs and VLMs due to the requirement of ground-truth data or identical distribution. The proposed method, Self-Comparison, uses paraphrasing to trigger the model’s memorization on training data without requiring access to ground-truth data. Extensive experiments demonstrate that this approach outperforms traditional methods across various datasets and models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper helps us understand how big language models and computer vision models are used in ways that might be against copyright laws. The researchers found a way to detect when these models are being misused, which is important because it can help prevent people from copying or stealing copyrighted materials without permission. They did this by looking at how the models work and creating a new method to figure out if the data they’re using is from the training process or not. This method is more effective than previous methods and can be used with different types of models and datasets. |
Keywords
» Artificial intelligence » Inference
